CN102081655A - Information retrieval method based on Bayesian classification algorithm - Google Patents
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Abstract
The invention discloses an information retrieval method based on a Bayesian classification algorithm in the technical field of information processing, which comprises the steps of: selecting a data sheet from a relationship database, establishing an information retrieval model; deriving a sample set from the information retrieval model, dividing the sample set into a training data set and a testing data set; selecting relevant fields from the information retrieval model to be used as a classification attribute of the sample set, determining the class of the classification attribute and calculating the prior probability of the class of the classification attribute; calculating the posterior probability of samples; calculating the class probability of the samples according to the Bayesian formula; classifying the samples according to the class probability of the samples, and generating a data classification set; and making the information retrieval operation in the data classification set by a user. The invention applies the Bayesian classification algorithm to the actual information retrieval, thereby effectively improving the accuracy of the information retrieval.
Description
Technical field
The invention belongs to technical field of information processing, relate in particular to a kind of information retrieval method based on the Bayes algorithm.
Background technology
Information retrieval is the important component part of internet, applications, and along with the growth at full speed of internet information, complicated taxonomic hierarchies makes to be retrieved more and more difficult to valuable information.
Bayes is the basic algorithm in the text mining, and this method is classified to text message quickly and accurately by utilization theory of probability knowledge; And sorted information can be used as the basis of other application.
Information category variation, the complicated present situation of information relationship cause the user according to demand, during query-related information, may extend to other message subjects by any one message subject, thereby constitute some information rings in system.This category feature often makes when system carries out information recommendation, and some literal similar, information of differing greatly of meaning actually may be provided, and causes the user may run into unpredictable trouble when carrying out information retrieval.
At the problems referred to above, the present invention in information retrieval, by information resources are classified, dwindles range of search with the Bayes algorithm application, thereby is implemented in when retrieving in a certain classification, can improve the accuracy rate of information retrieval.
Summary of the invention
The objective of the invention is to, a kind of information retrieval method based on the Bayes algorithm is provided, raw information is classified, to dwindle the scope of information retrieval by the Bayes algorithm, under a certain particular category, carry out information retrieval then, thereby improve the accuracy rate of information retrieval.
Technical scheme is that a kind of information retrieval method based on the Bayes algorithm is characterized in that described method comprises the following steps:
Step 1: from relational database, choose tables of data, set up the information retrieval model; Describedly set up the information retrieval model specifically: the major key and the external key of the described tables of data of definition earlier according to major key that exists between the tables of data and external key relation, make up ring texture information retrieval model then;
Step 2: from described information retrieval model, derive sample set, and sample set is divided into training dataset and test data set;
Step 3: from the information retrieval model, select the categorical attribute of relevant field, determine the classification of described categorical attribute as sample set, and according to other prior probability of categorical attribute compute classes P (C
i);
Step 4: the posterior probability P (X|C that calculates sample
i);
Step 5: according to Bayesian formula
Calculate the class probability P (C of sample
i| X);
Step 6: the class probability according to sample is classified to sample, and generates the data qualification collection;
Step 7: the user concentrates in data qualification and does the information retrieval operation.
Described prior probability P (C
i) be meant that training data concentrates the shared ratio of sample of each classification, prior probability P (C
i) utilize formula P (C
i)=s
i/ s calculates, wherein s
iBe classification C
iAt the sample number of training dataset, and s is the total sample number of training dataset.
Described posterior probability P (X|C
i) be meant that test data concentrates the sample proportion of each classification, utilize formula
Calculate, wherein, probability P (X
k| C
i)=s
Ik/ s
i, 1≤k≤n, s
IkBe classification C
iAt the sample number of test data set, and s
iBe the total sample number of training dataset, n is the classification number.
Described class probability according to sample is classified specifically to sample: the probability size of the sample of compare test data centralization under each classification, and choose the classification of probable value maximum sample is classified; Wherein, most probable value utilizes formula X ∈ C
i| P (C
i| X)=Max{P (C
i| X) } calculate.
Effect of the present invention is, with the information retrieval of Bayes algorithm application in reality, has improved the degree of accuracy of information retrieval effectively.
Description of drawings
Fig. 1 is based on the information retrieval method process flow diagram of Bayes algorithm;
Fig. 2 is an information retrieval modelling synoptic diagram;
Fig. 3 is based on the graphical example procedure figure of information retrieval of Bayes algorithm.
Embodiment
Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that following explanation only is exemplary, rather than in order to limit the scope of the invention and to use.
Fig. 1 is based on the information retrieval method process flow diagram of Bayes algorithm.Among Fig. 1, comprise the following steps: based on the information retrieval method of Bayes algorithm
Step 1: from relational database, choose tables of data, set up the information retrieval model.Fig. 2 is an information retrieval modelling synoptic diagram.Among Fig. 2, with scientific payoffs table, personal information table and the department information table chosen in database is example, according to the incidence relation between three tables, set up the information retrieval model specifically: the major key and the external key that define three tables of data earlier, according to major key that exists between three tables of data and external key relation, make up ring texture information retrieval model then.
Step 2: from the information retrieval model, derive sample set, and sample set is divided into training dataset and test data set.
Deriving sample set from the information retrieval model specifically is to derive the data recording that is used to make up the data qualification collection from constructed information retrieval model at random, and it is divided into training dataset and test data set, the data recording of training dataset and test data set is generally with 2: 1 ratio random division.Wherein, training dataset is meant have been demarcated, and is used for the data acquisition of training classifier.Test data set is meant not demarcation, need carry out the recognition data set with sorter.
Fig. 3 is based on the graphical example procedure figure of information retrieval of Bayes algorithm.Among Fig. 3, from constructed information retrieval model, select 1000 data at random as sample set.Wherein, 666 as training dataset, and 334 as test data set.
Step 3: from the information retrieval model, select the categorical attribute of relevant field, determine the classification of described categorical attribute as sample set, and according to other prior probability of categorical attribute compute classes P (C
i).
According to the actual requirements, this example is chosen four fields of sample as categorical attribute, is respectively functional localization attribute, subject attribute, avatar attribute and national economy industry attribute.Wherein, the functional localization attribute kit contains 12 classifications, and the subject attribute kit contains 58 classifications, and the avatar attribute kit contains 16 classifications, and national economy industry attribute kit contains 98 classifications.
In order to simplify calculating, this example is chosen the functional localization attribute and is calculated as categorical attribute, and subject attribute, avatar attribute are similar to the computing method of functional localization attribute with the computing method of national economy industry attribute, repeat no more here.
According to 12 classifications that the functional localization attribute comprises, calculate its prior probability P (C
i).Prior probability P (C
i) be meant that training data concentrates the shared ratio of sample of each classification, prior probability P (C
i) utilize formula P (C
i)=s
i/ s calculates, wherein s
iBe classification C
iAt the sample number of training dataset, and s is the total sample number of training dataset.In this example, the prior probability of 12 classifications of functional localization attribute is respectively 11.4%, 9.0%, 0.6%, 11.7%, 28.5%, 12.7%, 6.6%, 7.8%, 3.5%, 0.4%, 18.8% and 0.
Step 4: the posterior probability P (X|C that calculates sample
i).
Sample is meant a data recording in the sample set.Posterior probability P (X|C
i) be meant that test data concentrates the sample proportion of each classification, utilize formula
Calculate, wherein, probability P (X
k| C
i)=s
Ik/ s
i, 1≤k≤n, s
IkBe classification C
iAt the sample number of test data set, and s
iBe the total sample number of training dataset, n is the classification number.In this example, this is one dimension for sampling, so its posterior probability is set to 1.
Step 5: according to Bayesian formula
Calculate the class probability P (C of sample
i| X).
Bayesian formula
Be all to be separate between the category attribute of each sample of supposition, and each attribute is the same to the influence that given classification produces.Each data sample is with a n dimensional feature vector X={X in the formula
1, X
2..., X
nExpression, the vectorial C={C of the category attribute of sample
1, C
2..., C
mExpression, P (C
i) be prior probability, P (X
j| C
i) be posterior probability, P (X) is the total probability of sample, for each sample class, P (X) is a constant.
According to 12 classifications that the functional localization attribute comprises, the class probability value of calculating sample is respectively 11.4%, 9.0%, 0.6%, 11.7%, 28.5%, 12.7%, 6.6%, 7.8%, 3.5%, 0.4%, 18.8% and 0.Wherein, maximal value is 28.5%, so the sample class probability is 28.5%.
Step 6: the class probability according to sample is classified to sample, and generates the data qualification collection.
According to the probability size of sample under each category attribute that the class probability of sample is classified and is meant the compare test data centralization sample, choose the classification of probable value maximum sample is classified.Its most probable value is by formula X ∈ C
i| P (C
i| X)=Max{P (C
i| X) } calculate.Because of the class probability value maximal value of the sample that calculates in the step 5 is 28.5%, so in its corresponding class, sample is classified.
Step 7: the user concentrates in data qualification and does the information retrieval operation.
The user input query keyword is concentrated in completed data qualification and according to keyword to be retrieved.
For example, the user imports keyword for " large electric power plant unit ", and the user concentrates in completed data qualification and retrieves, and to obtain with " large electric power plant unit " be the expectation information of keyword, and the example partial content is as follows:
" research of Chinese large-sized fired power generating unit air cooling design and operation gordian technique and application ", " study and use ", " application of advanced control strategy in the large electric power plant unit and the exploitation of Control Software Package " based on the large electric power plant unit synthesis energy saving that Energy saving theory is analyzed.
If the user will retrieve next time, then re-enter keyword and retrieve; Otherwise finish retrieval.
The above; only for the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, and anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; the variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.
Claims (4)
1. the information retrieval method based on the Bayes algorithm is characterized in that described method comprises the following steps:
Step 1: from relational database, choose tables of data, set up the information retrieval model; Describedly set up the information retrieval model specifically: the major key and the external key of the described tables of data of definition earlier according to major key that exists between the tables of data and external key relation, make up ring texture information retrieval model then;
Step 2: from described information retrieval model, derive sample set, and sample set is divided into training dataset and test data set;
Step 3: from the information retrieval model, select the categorical attribute of relevant field, determine the classification of described categorical attribute as sample set, and according to other prior probability of categorical attribute compute classes P (C
i);
Step 4: the posterior probability P (X|C that calculates sample
i);
Step 5: according to Bayesian formula
Calculate the class probability P (C of sample
i| X);
Step 6: the class probability according to sample is classified to sample, and generates the data qualification collection;
Step 7: the user concentrates in data qualification and does the information retrieval operation.
2. a kind of information retrieval method based on the Bayes algorithm according to claim 1 is characterized in that described prior probability P (C
i) be meant that training data concentrates the shared ratio of sample of each classification, prior probability P (C
i) utilize formula P (C
i)=s
i/ s calculates, wherein s
iBe classification C
iAt the sample number of training dataset, and s is the total sample number of training dataset.
3. a kind of information retrieval method based on the Bayes algorithm according to claim 1 is characterized in that described posterior probability P (X|C
i) be meant that test data concentrates the sample proportion of each classification, utilize formula
Calculate, wherein, probability P (X
k| C
i)=s
Ik/ s
i, 1≤k≤n, s
IkBe classification C
iAt the sample number of test data set, and s
iBe the total sample number of training dataset, n is the classification number.
4. a kind of information retrieval method according to claim 1 based on the Bayes algorithm, it is characterized in that described class probability according to sample classifies specifically to sample: the probability size of the sample of compare test data centralization under each classification, choose the classification of probable value maximum sample is classified; Wherein, most probable value utilizes formula X ∈ C
i| P (C
i| X)=Max{P (C
i| X) } calculate.
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| CN102956023A (en) * | 2012-08-30 | 2013-03-06 | 南京信息工程大学 | Bayes classification-based method for fusing traditional meteorological data with perception data |
| CN103064939A (en) * | 2012-12-25 | 2013-04-24 | 深圳先进技术研究院 | Method and system for re-ordering data |
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Effective date of registration: 20140925 Address after: 102206 Changping District North Road, No. 2, Beijing Patentee after: North China Electric Power University Patentee after: State Grid Corporation of China Patentee after: INFORMATION & TELECOMMUNICATION COMPANY OF STATE GRID XINJIANG ELECTRIC POWER COMPANY Address before: 102206, Beijing, Changping District, Beijing Desheng outside the door, Zhu Xin, North China Electric Power University Patentee before: North China Electric Power University |